1. A PSO-LSTM Model of Offshore Wind Power Forecast considering the Variation of Wind Speed in Second-Level Time Scale
- Author
-
Mo Fei, Mei Rui, Tang Yiming, Chao Yuan, and Hong Wang
- Subjects
Schedule ,Wind power ,Article Subject ,Scale (ratio) ,Computer science ,business.industry ,Astrophysics::High Energy Astrophysical Phenomena ,General Mathematics ,General Engineering ,Engineering (General). Civil engineering (General) ,Wind speed ,Power (physics) ,Offshore wind power ,Electricity generation ,ComputerApplications_MISCELLANEOUS ,Physics::Space Physics ,QA1-939 ,Astrophysics::Solar and Stellar Astrophysics ,TA1-2040 ,business ,Mathematics ,Physics::Atmospheric and Oceanic Physics ,Smoothing ,Marine engineering - Abstract
To enable power generation companies to make full use of effective wind energy resources and grid companies to correctly schedule wind power, this paper proposes a model of offshore wind power forecast considering the variation of wind speed in second-level time scale. First, data preprocessing is utilized to process the abnormal data and complete the normalization of offshore wind speed and wind power. Then, a wind speed prediction model is established in the second time scale through the differential smoothing power sequence. Finally, a rolling PSO-LSTM memory network is authorized to realize the prediction of second-level time scale wind speed and power. An offshore wind power case is utilized to illustrate and characterize the performance of the wind power forecast model.
- Published
- 2021